Recently, there have been initial studies that introduce some of the O-RAN technology and possible use cases for improving the performance of cellular networks. Reference [1]} demonstrates the potentials of the O-RAN framework for radio resource management by applying AI on a dataset provided by a network operator. A green O-RAN system model is proposed in [2]}, which introduces the use of renewable energy sources and how to minimize the cost of deployment by applying reinforcement learning for dynamic function splitting.
Reference [3]} discusses the potentials of O-RAN and a few limitation while providing a data-driven closed-loop control experiment with reinforcement learning agents for optimizing the scheduler of multiple RAN slices. A closed-loop control system for O-RAN is designed and validated on an outdoor testbed for maximizing the capacity of the system composed of an aerial and a terrestrial UE by optimizing the drone location and transmission directionality [4]}.
